The basic process k center algorithm is: First free to choose a delegate object for each cluster, the rest of the object based on its distance each of which represents the object (here the distance is not necessarily a Euclidean distance, Manhattan distance may be) allocated to Recently representatives object represents a cluster and then repeatedly with representatives of non-objects instead represents the object, in order to optimize the quality of clustering. Clustering quality is represented by a cost function. When a center is a non-center alternative, in addition to not replace the center, the rest of the points to be reallocated.

Cluster_K-means
...............\Cluster_K-means.sdf
...............\Cluster_K-means.sln
...............\Cluster_K-means.v12.suo
...............\Cluster_K-means.vcxproj
...............\Cluster_K-means.vcxproj.filters
...............\Debug
...............\.....\Cluster_K-means.exe
...............\.....\Cluster_K-means.ilk
...............\.....\Cluster_K-means.log
...............\.....\Cluster_K-means.pdb
...............\.....\Cluster_K-means.tlog
...............\.....\....................\CL.read.1.tlog
...............\.....\....................\CL.write.1.tlog
...............\.....\....................\Cluster_K-means.lastbuildstate
...............\.....\....................\link.read.1.tlog
...............\.....\....................\link.write.1.tlog
...............\.....\main.obj
...............\.....\vc120.idb
...............\.....\vc120.pdb
...............\main.cpp

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